Given a malfunctioning system, sequential diagnosis aims at identifying the root cause of the failure in terms of abnormally behaving system components. As initial system observations usually do not suffice to deterministically pin down just one explanation of the system's misbehavior, additional system measurements can help to differentiate between possible explanations. The goal is to restrict the space of explanations until there is only one (highly probable) explanation left. To achieve this with a minimal-cost set of measurements, various (active learning) heuristics for selecting the best next measurement have been proposed. We report preliminary results of extensive ongoing experiments with a set of selection heuristics on real-world diagnosis cases. In particular, we try to answer questions such as "Is some heuristic always superior to all others?", "On which factors does the (relative) performance of the particular heuristics depend?" or "Under which circumstances should I use which heuristic?"

翻译：鉴于系统失灵,连续诊断旨在从异常行为系统组成部分中找出失败的根本原因。由于最初的系统观测通常不足以确定系统错误行为的唯一解释,额外的系统测量有助于区分可能的解释。目标是限制解释的空间,直到只有一个(极有可能的)解释留下。为了达到这个目的,提出了一套最低成本的测量方法,为选择最佳的下一个测量方法提出了各种(积极学习)的累赘。我们报告了一系列正在广泛进行的实验的初步结果,其中对现实世界诊断案例作了一套选择的累赘。特别是,我们试图回答一些问题,例如“某些超自然学总是优于所有其他人吗?” 、“某些超自然学的性能取决于哪些因素?”或“在哪些情况下我应该使用哪些超自然学?”